Engineering natural microbiomes toward enhanced bioremediation by microbiome modeling

Engineering natural microbiomes for biotechnological applications remains challenging, as metabolic interactions within microbiomes are largely unknown, and practical principles and tools for microbiome engineering are still lacking. Here, we present a combinatory top-down and bottom-up framework to engineer natural microbiomes for the construction of function-enhanced synthetic microbiomes. We show that application of herbicide and herbicide-degrader inoculation drives a convergent succession of different natural microbiomes toward functional microbiomes (e.g., enhanced bioremediation of herbicide-contaminated soils). We develop a metabolic modeling pipeline, SuperCC, that can be used to document metabolic interactions within microbiomes and to simulate the performances of different microbiomes. Using SuperCC, we construct bioremediation-enhanced synthetic microbiomes based on 18 keystone species identified from natural microbiomes. Our results highlight the importance of metabolic interactions in shaping microbiome functions and provide practical guidance for engineering natural microbiomes.

Supplementary Fig. 13.Simulations of the biomass of strain X-1, 7D-2 or H8 growing separately versus in co-cultures (X-1&7D-2 or 7D-2&H8).The simulations were performed in the BO  Biomass in model Supplementary Fig. 14.Simplified metabolic network depicting metabolic interactions between strains X-1 and 7D-2.a, c The predicted reactions active in the strains X-1 (a) and 7D-2 (c).The codes starting with "rxn" represent metabolic reactions.Supplementary Table 7 provides detailed information on these reactions, including reactants, products, stoichiometry, direction, ΔG, and genes coding the enzymes required for the metabolic interactions.The heatmap shows gene expression levels of strains X-1 and 7D-2 in single-culture and co-culture, and each treatment had three replicates (n = 3 biological independent replicates).Red indicates increased transcript abundance, while blue indicates decreased transcript abundance.The red arrows indicate up-regulated genes in co-cultures compared to single-cultures.b Prediction and experimental validation of exchanged metabolites between strains 7D-2 and X-1.The data are presented as mean values ± SD (n = 3 biological independent replicates).The bar graph displays the concentration of each exchanged metabolite detected by LC-MS in the medium of co-cultures (BO&X-1&7D-2).Medium with bromoxynil octanoate (BO) as the only carbon and nitrogen source was used for both simulation and experimental validation.The medium without inoculation of any strains was used as control.Notably, these exchanged metabolites were not initially present in the medium, and their detection validates the secretion of these metabolites by the strains.Source data are provided as a Source Data file.

Staphylococcus warneri R93
Terrabacter lapilli R-1-41 -Ra, the electron-acceptor equation; Rd, the electron-donor equation; Re, the energy equation (Re = Ra -Rd); Rc, the cell synthesis equation; Rs, the synthesis equation (Rs = Rc -Rd); R, the overall equation that includes energy generation and synthesis (R = fe * Re + fs * Rs); fe, the portion of electrons used for energy (here, fe = 0.6); fs, the portion of electrons used for cell synthesis (here, fs = 0.4).The free energies of formation for various chemical species (Δ f G) used in Ra, Rd and Re are obtained from eQuilibrator (https://equilibrator.weizmann.ac.il/) 1 .As no Δ f G for bromoxynil in any public database or literature, we used Δ f G for 4-Hydroxybenzoate (the first intermediate metabolite of bromoxynil degradation with available Δ f G) for calculation.The empirical biomass formula for bacterial cell, C 5 H 7 O 2 N, was used 2 .
Supplementary Table 5.The overall energy generation and energy requirement for cell synthesis.ΔGs, the energy requirement for cell synthesis; ΔGr, the energy released per equivalent of donor oxidization for energy generation; Y, the yield of cells.These results show that the predicted metabolic interaction is thermodynamic feasible (ΔGs + ΔGr < 0).
Supplementary Table 6.Detailed information about the metabolic reactions related to the metabolic interactions between strains X-1 and 7D-2.The standard Gibbs energy of reaction for each reaction (Δ r G' o ) were calculated at pH 7.0, ionic strength of 0.

Supplementary Fig. 15 .
Experimental validations of the function of exchanged metabolites (EMs) in two-member consortia.a, b Predicted metabolic interactions in 7D-2&X-1 (a) and 7D-2&H8 (b) by modeling.Eight testable predictions about the function of exchanged metabolites were provided for experimental validation (c-f).The bar plots show the growth and fraction of BO/DBHB left in medium containing BO/DBHB as a sole nitrogen and carbon source versus the same medium supplemented by exchange metabolites.The growth and degradation were enhanced in the supplemented medium for strains X-1, 7D-2, and H8, indicating the exchanges could be additional carbon and/or nitrogen source for degraders which increased the biomass of degrader and finally improve the degradation.BO, bromoxynil octanoate; DBHB, 3,5-dibromo-4-hydroxybenzoate; BRO, bromoxynil.The data are presented as mean values ± SD (n = 3 biological independent replicates).Source data are provided as a Source Data file.Supplementary Fig. 16.LC-MS analysis of the exchanged metabolites in single-cultures and co-cultures.a Secretion of hypoxanthine by X-1.b Secretion of succinate by 7D-2.c Secretion of glutamate by 7D-2.For single-cultures, the strains X-1 and H8 were cultured in the MM medium containing BO and DBHB respectively, while the strain 7D-2 was cultured in the MM medium containing Bro or DBHB.The co-cultures of X-1&7D-2 and 7D-2&H8 were cultured in the MM medium containing BO and DBHB respectively.All final cultures were tested after 4 hours of culture.BO, bromoxynil octanoate; Bro, bromoxynil; DBHB, 3,5-dibromo-4-hydroxybenzoate.Supplementary Fig. 17.Simulations and experimental validations of metabolic interactions in two-member consortia.a, b Predicted changes in the biomass ratio of strains 7D-2 and X-1 according to bromoxynil octanoate (BO) content in the medium (a) and experimental validation (b), showing that the biomass ratio of the two strains increased with decreasing BO content.For b, the data are presented as mean values ± SD (n = 3 biological independent replicates).c The biomass ratios of 7D-2 and X-1 in soils treated with BO-7D-2&X-1 increased over time, consistent with the prediction (n = 4 biological independent replicates).P, purple soil; Y, yellow cinnamon soil; R, red soil.Linear regression line is indicated by the colored line.The 95% confidence interval of the linear regression line is indicated by gray bands.P values are two-sided.Source data are provided as a Source Data file.Supplementary Fig. 18.Predicted biomass of bacterial combinations by SuperCC.The grey/white cells in grids indicate species included/not included in the bacterial combination, respectively.The bars on the left of the grids are indicative of biomass predicted in three media: DBHB medium supplemented with NH 4 + (DBHB.NH 4 + ) as the sole carbon and nitrogen source, DBHB medium supplemented with NO 3 -(DBHB.NO 3 -), and DBHB medium supplemented with glucose and NH 4 + (DBHB.

Table 2 .
ASV information of keystones and comparison with identity of genomes.

forest analysis Isolates Identity a Genome accession * Identity b
a Similarity between 16s of the ASV and of respective sequence of selected strains.bSimilarity between 16s of the selected strains and of respective sequence in the genome sequence.*Genomes of the mostly closely related species of each strain with available genome sequences were chosen for model construction.Supplementary

Table 3 .
Strains used in the study and general features of the metabolic models constructed for

Table 4 .
Half-reactions and their Gibb's standard free energy at PH = 7.0.